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26 papers · ranked by Valyu relevance

01
AarXiv2022
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Ling Chen, Donghui Chen, Zongjiang Shang, Youdong Zhang + 2 more
'Yang Cheng-hu'] Abstract—Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies. Existing works only learn temporal…
85relevance
02
PPubMed23 Dec 2021
Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers
Yuying Liu, J. Nathan Kutz, Steven L. Brunton
Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration expensive. In this work, we develop a…
85relevance
03
BbioRxiv22 Sep 2023
Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations
Yin-Jui Chang, Yuan-I Chen, Hannah M. Stealey, Yi Zhao + 5 more
Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective…
83relevance
04
BbioRxiv15 Nov 2023
A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network
Alireza Gharahi, Majid Mohajerani
The multi scale architecture by Breakspear and Stam [2] introduces a framework to consider the dynamical processes specific to a nested hierarchy of spatial scales, from neuronal masses to cortical columns and functional brain regions. They hypothesize that the neural dynamics is a function of the structural properties…
83relevance
05
AarXiv2022
Progressive Multi-scale Light Field Networks
David Li, Amitabh Varshney
Neural representations have shown great promise in their ability to represent radiance and light fields while being very compact compared to the image set representation. However, current representations are not well suited for streaming as decoding can only be done at a single level of detail and requires downloading…
83relevance
06
PPubMed1 Mar 2024
Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
Yin-Jui Chang, Yuan-I Chen, Hsin-Chih Yeh, Samantha R. Santacruz
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the…
83relevance
07
PPubMed23 May 2022
Birdsong classification based on ensemble multi-scale convolutional neural network
Jiang Liu, Yan Zhang, Danjv Lv, Jing Lu + 4 more
'Yue Yin' 'Haifeng Xu'] With the intensification of ecosystem damage, birds have become the symbolic species of the ecosystem. Ornithology with interdisciplinary technical research plays a great significance for protecting birds and evaluating ecosystem quality. Deep learning shows great progress for birdsongs…
79relevance
08
PPubMed24 May 2025
Neural Encoding and Decoding at Scale
Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre + 5 more
Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural…
77relevance
09
AarXiv20 Apr 2026
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty + 17 more
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.1 million neurons from the visual cortex of 73 mice across 323 sessions, totaling more than 150…
77relevance
10
CchemRxiv16 May 2022
Neural Scaling of Deep Chemical Models
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
76relevance
11
BbioRxiv30 May 2023
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
Parima Ahmadipour, Omid G. Sani, Bijan Pesaran, Maryam M. Shanechi
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important…
75relevance
12
AarXiv13 Dec 2025
Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference
Eray Erturk, Maryam M. Shanechi
Real-time decoding of target variables from multiple simultaneously recorded neural time-series modalities, such as discrete spiking activity and continuous field potentials, is important across various neuroscience applications. However, a major challenge for doing so is that different neural modalities can have…
75relevance
13
AarXiv24 May 2025
Neural Encoding and Decoding at Scale
Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre + 6 more
'Zixuan Wang' 'Hanrui Lyu' 'The International Brain Laboratory' 'Eva Dyer' 'Liam Paninski' 'Cole Hurwitz'] Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus…
73relevance
14
PPubMed23 Jul 2024
Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution
Yizi Zhang, Yanchen Wang, Donato Jiménez Benetó, Zixuan Wang + 6 more
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a…
72relevance
15
CchemRxiv22 Apr 2025
Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems
Authors not listed
Multiscale modeling of complex chemical systems—ranging from polymers to biomolecules—requires coarse-grained (CG) techniques to bridge atomic-scale interactions with mesoscopic behavior. Traditional CG methods rely on handcrafted potentials, limiting their transferability across systems. We propose a…
70relevance
16
PPubMed23 Feb 2023
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
70relevance
17
BbioRxiv24 Sep 2024
*BrainScale*: Enabling Scalable Online Learning in Spiking Neural Networks
Chaoming Wang, Xingsi Dong, Jiedong Jiang, Zilong Ji + 2 more
Whole-brain simulation stands as one of the most ambitious endeavors of our time, yet it remains constrained by significant technical challenges. A critical obstacle in this pursuit is the absence of a scalable online learning framework capable of supporting the efficient training of complex, diverse, and large-scale…
70relevance
18
CchemRxiv31 Dec 2025
LAMMPS-ANI: Large Scale Molecular Dynamics Simulations with ANI Neural Network Potential
Authors not listed
Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This…
67relevance
19
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
67relevance
20
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
66relevance
21
PPubMed20 Jun 2023
A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies A multi-level account of the hippocampus
Robert M. Mok, Bradley C. Love
A complete neuroscience requires multilevel theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. We propose an extension to the level of mechanism approach where a computational model of cognition sits in between behavior and brain: It explains the higher-level…
65relevance
22
AarXiv18 Nov 2024
Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
Anmol Biswas, Sharvari Ashok Medhe, Raghav Singhal, Udayan Ganguly
Machines Authors: ['Anmol Biswas' 'Sharvari Ashok Medhe' 'Raghav Singhal' 'Udayan Ganguly'] Abstract—Reservoir computing (RC), which is an umbrella term for a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) can be thought of as describing a generic method to perform…
64relevance
23
BbioRxiv8 Sep 2026
Biologically grounded locality priors close the data gap for vision transformers in neural prediction
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
64relevance
24
BbioRxiv27 Sep 2022
A scalable implementation of the recursive least-squares algorithm for training spiking neural networks
Benjamin J. Arthur, Christopher M. Kim, Susu Chen, Stephan Preibisch + 1 more
Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU…
62relevance
25
BbioRxiv26 Jul 2024
Structured flexibility in recurrent neural networks via neuromodulation
Julia C. Costacurta, Shaunak Bhandarkar, David M. Zoltowski, Scott W. Linderman
The goal of theoretical neuroscience is to develop models that help us better understand biological intelligence. Such models range broadly in complexity and biological detail. For example, task-optimized recurrent neural networks (RNNs) have generated hypotheses about how the brain may perform various computations…
60relevance
26
AarXiv13 Oct 2025
Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
Alboré, Nicola, Di Antonio, Gabriele + 4 more
We propose a new reservoir computing method for forecasting high-resolution spatiotemporal datasets. By combining multi-resolution inputs from coarser to finer layers, our architecture better captures both local and global dynamics. Applied to Sea Surface Temperature data, it outperforms standard parallel reservoir…
60relevance

Results for Multi-scale Neural Models

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network

Progressive Multi-scale Light Field Networks

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Birdsong classification based on ensemble multi-scale convolutional neural network

Neural Encoding and Decoding at Scale

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Neural Scaling of Deep Chemical Models

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference

Neural Encoding and Decoding at Scale

Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution

Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

*BrainScale*: Enabling Scalable Online Learning in Spiking Neural Networks

LAMMPS-ANI: Large Scale Molecular Dynamics Simulations with ANI Neural Network Potential

Multi-fidelity machine learning models for improved high-throughput screening predictions

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies A multi-level account of the hippocampus

Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Structured flexibility in recurrent neural networks via neuromodulation

Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling

Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Hierarchical deep learning of multiscale differential equation time-steppers Hierarchical deep learning of multiscale differential equation time-steppers

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network

Progressive Multi-scale Light Field Networks

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Birdsong classification based on ensemble multi-scale convolutional neural network

Neural Encoding and Decoding at Scale

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Neural Scaling of Deep Chemical Models

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference

Neural Encoding and Decoding at Scale

Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution

Learning Hierarchical Coarse-Grained Models for Complex Chemical Systems

Abstract representations emerge naturally in neural networks trained to perform multiple tasks

*BrainScale*: Enabling Scalable Online Learning in Spiking Neural Networks

LAMMPS-ANI: Large Scale Molecular Dynamics Simulations with ANI Neural Network Potential

Multi-fidelity machine learning models for improved high-throughput screening predictions

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies A multi-level account of the hippocampus

Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Structured flexibility in recurrent neural networks via neuromodulation

Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling

BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks

BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks